language:
- zh
license: apache-2.0
tags:
- bert
- deberta
inference: true
widget:
- text: 桂林是世界闻名的旅游城市,它有[MASK]江。
Erlangshen-DeBERTa-v2-320M-Chinese，one model of Fengshenbang-LM
The 320 million parameter deberta-V2 base model, using 180G Chinese data, 8 A100(80G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 250M samples. our model is still training. And we will update our model once a week!
Task Description
Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta paper
Usage
from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
import torch
tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese', use_fast=False)
model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese')
text = '桂林是世界闻名的旅游城市,它有[MASK]江。'
fillmask_pipe = FillMaskPipeline(model, tokenizer, device=0)
print(fillmask_pipe(text, top_k=10))
Finetune
We present the dev results on some tasks.
| Model | AFQMC | TNEWS1.1 | IFLYTEK | OCNLI | CMNLI | | -------------------------------------------------------------------------------------------------------------------------------- | ------ | -------- | | RoBERTa-base | 0.7406 | 0.575 | 0.6036 | 0.743 | 0.7973 | | RoBERTa-large | 0.7488 | 0.5879 | 0.6152 | 0.777 | 0.814 | | IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese | 0.7405 | 0.571 | 0.5977 | 0.752 | 0.7568 | 0.807 | | IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese | 0.7498 | 0.5817 | 0.6042 | 0.8022 | 0.8301| | Erlangshen-Deberta-XLarge-710M-Chinese | 0.7549|0.5873|0.6177|0.8012|0.8389|
Citation
If you find the resource is useful, please cite the following website in your paper.
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2022},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}